The rapid pace of technological advancement has spurred the development of a range of electronic commerce (e-commerce) applications and data available on the Web. Since information is distributed in different sites or databases, applications have to query on the them in order to get the "big picture". The challenge is how to process and integrate distributed data to meet rapidly-changing business requirements in a flexible way. On another front, mobile devices such as Personal Data Assistances (PDAs) are fast emerging as components of our daily life. Here, the challenge is to identify the limitations and opportunities to query distributed data sources wirelessly on mobile devices. This study addresses both these important e-commerce challenges with different data integration technologies.

Data processing and integration on heterogeneous data sources often requires intensive human resources on coding, but applications developed for this

purpose are usually highly customized and difficult to reuse. To meet the dynamic business requirements of the Internet age, we propose a Web Services integration framework in which all operations and data sources are exposed as Web Services. Based on the framework, a novel declarative XML scripting language called WSIPL (Web Services Integration and Processing Language) is designed to drive data integration tasks in a flexible manner. Companies which have deployed this technology have found that WSIPL can successfully enhance their data integration and processing systems by providing greater flexibility and efficiency.

In a wireless data transmission environment, users of mobile devices are typically charged by the amount of data transferred, rather than by the amount of time they stay connected. The study proposes an algorithm, known as RAMJ (Recursive and Adaptive Mobile Join), to integrate (join) information from noncollaborative remote databases on mobile devices. RAMJ dynamically and adaptively collects statistics and uses them to optimize the overall join process, by taking the limitations of mobile devices (e.g., small RAM and no physical disk) into account. Experimental results show that this approach can effectively reduce the amount of downloaded data during join processing.